Title :
Output feedback control of a robotic exoskeleton with input deadzone via neural networks
Author :
Ziting Chen;Wei He;Yiting Dong;Zhijun Li
Author_Institution :
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
fDate :
6/1/2015 12:00:00 AM
Abstract :
In this paper, adaptive output feedback control via neural networks is designed for a robotic exoskeleton with unknown dynamics. Neural networks are used to compensate for the unknown deadzone effect induced by the actuators and the unknown dynamics of the robot. High-gain observer is employed to estimate the velocity information and then integrated in the design of output feedback controller. The deadzone effect is approximated by a Radial Basis Function Neural Network (RBFNN) and the tracking error for the deadzone effect is bounded and converging. The unknown dynamics of the robotic exoskeleton are estimated with another RBFNN. The proposed control is able to compensate for the estimated deadzone effect and track the desired trajectory. Finally, numerical simulation and experiment on a two-joint rigid exoskeleton demonstrate the effectiveness of the proposed method.
Keywords :
"Robots","Neural networks","Output feedback","Exoskeletons","Actuators","Trajectory"
Conference_Titel :
Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on
Print_ISBN :
978-1-4799-8728-3
DOI :
10.1109/CYBER.2015.7288273